BiLSTM-CRF is a neural network architecture that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Conditional Random Fields (CRF) for sequence labeling tasks, providing both contextual understanding and structured prediction. This model excels in tasks like named entity recognition by leveraging BiLSTM's ability to capture dependencies in both directions and CRF's capability to model output label dependencies.